Recent advances in the Human Genome project have, paradoxically, led to the wide-spread recognition of the inadequacy of gene sequence information by itself. Sequence information (i.e. structural genomics) is unlikely to generate insight into disease or normal physiology without better information concerning the functional consequence of genes. Higher levels of biological organization relative to gene sequences include expressed mRNA levels, the expressed protein complement and concentrations of organic molecules in metabolic pathways. These levels of cellular organization have been called gene expression profiling (transcriptomics), proteomics and organeomics, respectively. In aggregate, these can be seen as including the structural biochemical phenotype (i.e. the complete complement of molecules present) in a cell or organism.
Gene expression profiling of mRNA has been achieved through the development of gene expression chips. Such chips are available from companies such as Affymetrix. Enumerating the expressed genome (i.e. the complement of mRNA species), even in its entirety, however, does not ultimately provide information about biochemical function (phenotype) in a living system. Although impressive as a technology, gene expression chips do not solve the central problems of phenotype and function in biochemistry, which relate to the flow of molecules through the complex interactive network of proteins that comprise fully assembled living systems.
Other methods have focused on characterizing the complement of proteins in a living system, i.e. the “proteome.” The most powerful technology for automated, large-scale characterization of expressed proteins (proteomics) to date has proven to be mass spectrometry. Mass spectrometers have greatly simplified large-scale automated proteome analysis. Analogous mass spectrometric methods have been advanced for the automated, large-scale characterization of organic metabolites (organeomics).
Many scientists in the pharmaceutical industry, including those in genomics companies, are predicting a log-jam of potential drug leads and targets that are under development. This log-jam arises from bottle-necks in the testing of phenotypic consequences of inhibiting particular targets—i.e. the “tail-end” of drug development. The tail-end of drug development (target validation) has not received a similar push from breakthrough technologies as the “front-end” (target identification and identification of chemical modulators of targets) of drug discovery.
One of the central problems in this area relates to the absence of routine, high-throughput dynamic measurements in biology and medicine. Just as biochemical phenotype is recognized to be reducible to the flow of molecules through metabolic pathways in complex catalytic networks, it is also widely recognized that most diseases reduce to an altered rate of a normal process. For example, atherogenesis reflects vascular wall proliferation and uptake of lipids; carcinogenesis reflects cell proliferation; infection can be characterized as microbial division, growth and death—this formulation is more informative than describing these disorders as alterations of static measures (e.g. concentrations of cholesterol, carcinogens, or bacteria). Yet rarely, if ever, are rates of biochemical processes measured in medical diagnostics. Static markers of dynamic processes are often helpful and may be better than nothing, but they are not the true measure of disease activity or disease risk. Nor do static measures allow for personalized biochemical monitoring. For example, each individual may have a different relationship between CD4 count and true turnover of T lymphocytes in HIV infection, or between DNA-adducts and the true risk of cancer, or between LDL cholesterol and the true rate of atherogenesis. In the final analysis, kinetic questions must be addressed by direct kinetic measurements.
Thus, the current art in mass spectrometric proteomics and organeomics is characterized by a shared and fundamental limitation: the information is static, not dynamic. Missing from both static proteomics and static organeomics is kinetics: fluxes into and out of the pools of molecules that are present in the system. Kinetics or dynamics differ from statics in the fundamental respect that the dimension of time is induded. Kinetics refers to the study of time-related changes in molecules whereas the concentrations of proteins or organic molecules determined in static measurements do not provide any information about their rates of change over time. Although the current techniques of static proteome and organeome characterization can provide a snapshot of what is present, these techniques cannot provide information concerning flows of molecules through the system (kinetics).
Thus, there is a tremendous need for the large scale determination of molecular flux rates of a plurality of proteins or organic metabolites—i.e. “dynamic proteomics” and “dynamic organeomics”.